An abstract state machine semantics for discrete event simulation

Network centric operations (NCO) has been dubbed the most significant revolution in military affairs (RMA) in the past 200 years. The promise of NCO is based on the notion that information sharing and connectivity is fundamental to the effectiveness of a combat force. This is due to the ability of a properly networked force to self-synchronise itself as it engages enemy forces. The information age combat model (IACM) is a popular representation of NCO. Previous research at modelling the IACM has so far focused solely at using agent-based modelling. That work produced significant contributions to the IACM literature. However, it has proven to be computationally expensive. IACM simulations must be sustainable as research into IACM evolves and more NCO complexities are introduced. This research will propose a discrete-event simulation (DES) approach to model the IACM. The paper will present the basis upon which DES was selected for this purpose and will illustrate how the DES approach can provide significant improvements in terms of the time and computational power needed to run IACM simulations.

The standard clock (SC) method is an efficient approach for discrete event simulation. Its basic ideas are quite different from traditional approaches. SC has neither an event list nor event lifetimes. However, its applicability is limited to exponential distributions and a class of non-exponential distributions. In this paper we provide an effective approach to extend SC to all general distributions, while preserving the efficiency advantage of SC. Numerical testing shows that our approach can effectively accomplish this goal.

We show how a basic discrete event simulation language can be enabled for business process modeling and simulation by adding an activity construct. While activities are often not considered at all or not treated in a conceptually satisfactory way in the discrete event simulation literature, the great majority of business process modeling languages are based on an activity construct. However, unlike a simulation language, the predominant business process modeling languages, including UML Activity Diagrams and the Business Process Modeling Notation (BPMN), are not executable. So, the challenge for business process modeling is to define an executable semantics for activities, while the challenge for discrete event simulation is to find a way how to introduce an activity construct on top of the basic discrete event simulation concepts of objects and events. The main idea is to define an activity as a complex event having a start event and an end event. This idea is well-known from the business process modeling literature, e.g. from the Business Process Definition Metamodel (Bock 2008), but it has also been used in a rudimentary way in some discrete event simulation approaches, e.g. in (Fishman 2001). Our research contribution consists of two achievements: 1) we define a conceptual model of activities for discrete event simulation and implement it in our ER/AOR simulation language; 2) we show how to use BPMN for the purpose of simulation modeling.

The aim of this research is to create a simulation model for a gas station in Changlun, Kedah. This model is intended to identify bottlenecks and propose alternative solutions to enhance system performance and improve customer satisfaction. The study uses the discrete event simulation (DES) approach to develop and analyse the operational system of a specific gas station in Changlun. Performance metrics considered in this model include average and maximum queue waiting times, average and maximum total system times, average and maximum queue lengths, and utilisation rates. The simulation model is developed using Arena software, and the resulting data is analysed. Based on the findings, three potential solutions are proposed: designating pumps exclusively for motorcycles, expanding parking space, and combining both approaches.

Developing a sustainable manufacturing system is a progressively challenging issue as governments across the world have been enforcing increasingly severe regulations by promoting the reduction of environmental waste for manufacturing and energy-saving production activities. Thus, there is a need for developing a sustainable manufacturing system that can be fully examined by incorporating ecological aspects (e.g., consumed energy) for related operations of a manufacturing system using computer-based discrete event simulation tools. In this study, a combined framework of a novel hybrid fuzzy multi-objective optimization and discrete event simulation approach is presented. We combine ecological and economic data and optimization techniques that are aimed at minimizing economic and ecological objectives in a manufacturing system at an early design phase. Hence, the fuzzy multi-objective optimization model is formulated by incorporating economic and ecological parameters. Again, the discrete event simulation model is established based on a comprehensive performance evaluation of the production system. This study also supports design decisions in determining optimum machine numbers, lighting, and cooling equipment required for the production processes within the sustainable manufacturing in conjunction with the most effective level of material flows. In addition, an integrated Decision-Making Trial and Evaluation Laboratory (DEMATEL)-epsilon constraint approach is applied to handle the multiple-objective optimization problem towards a set of trade-offs among the optimization objectives. A real-life application is carried out for investigating the applicability of the created hybrid framework. The findings of this study demonstrate that this framework is useful as a decision-making tool since it can develop a sustainable manufacturing system design considering an optimal solution associated with amounts of energy usage and CO2 emissions under economic constraints.

This research aims to develop a simulation theory using discrete event simulation as a convincing tool for building a digital twin models in the pharmaceutical industry calibration department as part of the supporting components enabler for industry 4.0. The Methodology used are define the Calibration Laboratory service model, then analyze the big data collected from the service performance parameters. The analyzed data will be used to continue the development of discrete event simulation models for calibration laboratory service systems using ProModel2016 software. The simulation output data will be verified with real event data to ensure similarity. The study finds that the Discrete Event Simulation approach can be used as a convincing tool to develop digital twin models as virtual replicas of Calibration Laboratory Services in the Pharmaceutical Industry so that improvement planning can be analyzed efficiently. There is a limitation of this research that the digital twin model can only be verified for the Pharmaceutical Industry Calibration Laboratory Services as a case study object. Further research needs to be carried out to expand the possibilities of using this discrete event simulation approach for Digital Twin Model in every aspect of industrial activities.

Simulations have significantly contributed to the analysis of construction, and Discrete Event Simulation has been a primary means of simulation focusing on Construction Operation. In this paper, the authors address the importance of Construction Context as another realm for understanding and managing construction through representation of overall behavior of construction associated with Construction Operation. The issues raised from representing both Construction Operation and Construction Context together in a simulation model are discussed, and a hybrid System Dynamics and Discrete Event Simulation approach is proposed as a comprehensive simulation framework for that purpose. Briefly, its application to construction is also discussed.

Prospective Health Technology Assessment allows early decision making for innovative health care technologies. The main idea is to combine available domain knowledge with advanced simulation techniques in order to predict the effects of medical products and to find bottlenecks and weaknesses within the health system. In our recent publications a hybrid simulation approach with System Dynamics and Agent-Based Modeling has been presented. Hospital workflows have been modeled by state charts within agent behavioral models and have to be instantiated each time an agent is entering a hospital. This paper presents a mechanism to generate agents dynamically from SD models and extends the previously presented hybrid approach by process-oriented Discrete Event Simulation for hospital modeling. It connects processes to health care institutions and not to persons traversing them. Two extended example case studies show potentials for medical decision making using the three simulation paradigms in a common environment.

Custom Hiring Centers (CHCs) for agricultural equipment and services are being promoted by the Government of India and various state governments. CHCs are important part of the complex agricultural system. They are beneficial to the community as small and marginal farmers, instead of having to purchase costly machines, can rent the machines as needed. Key factors that affect profitability are the type and number of equipment hosted by the CHC. Each piece of equipment entails a sunk cost as well as maintenance cost. However, since most agricultural operations are time sensitive, the demand is concentrated in time, and having too few pieces of equipment would entail inability to serve the demand and lost revenue. This paper presents a model to simulate the operations of a CHC, under varying scenarios, in order to estimate its profitability. The model uses a combination of Agent Based Modeling (ABM) and Discrete Event Simulation (DES) approaches. Farmers and equipment are modelled as the agents in the system while the CHC operations are modelled using DES approach. The use of the model is also demonstrated with the help of an example. The results showed that even for one combination of equipment, the profitability varies a lot. This significant variation in profitability arises from variation in the percentages of areas under different types of crop, as well as the inherent variation in crop growth rates, readiness of individual fields for particular operations within the seasonal window, as well as propensity of individual farmers to wait for CHC equipment if not immediately available. Results also demonstrate that simulation is useful to model CHC operations in a complex agricultural system and take informed decision about number and types of equipment to buy.

Comparison between discrete event simulation approach and various existing empirically-based models for estimation of TBM utilization

This paper describes a multi-level system dynamics (SD) / discrete event simulation (DES) approach for assessing planning and scheduling problems within an aviation training continuum. The aviation training continuum is a complex system, consisting of multiple aviation schools interacting through interschool student and instructor flows that are affected by external triggers such as resource availability and the weather. SD was used to model the overall training continuum at a macro level to ascertain relationships between system entities. SD also assisted in developing a shared understanding of the training continuum, which involves constructing the definitions of the training requirements, resources and policy objectives. An end-to-end model of the continuum is easy to relate to, while dynamic visualisation of system behaviour provides a method for exploration of the model. DES was used for micro level exploration of an individual school within the training continuum to capture the physical aspects of the system including resource capacity requirements, bottlenecks and student waiting times. It was also used to model stochastic events such as weather and student availability. DES has the advantage of being able to represent system variability and accurately reflect the limitations imposed on a system by resource constraints. Through sharing results between the models, we demonstrate a multi-level approach to the analysis of the overall continuum. The SD model provides the school’s targeted demand to the DES model. The detailed DES model is able to assess schedules in the presence of resource constraints and variability and provide the expected capacity of a school to the high level SD model, subjected to constraints such as instructor availability or budgeted number of training systems. The SD model allows stakeholders to assess how policy and planning affect the continuum, both in the short and the long term. The development of this approach permits moving the analysis of the continuum between SD and DES models as appropriate for given system entities, scales and tasks. The resultant model outcomes are propagated between the continuum and the detailed DES model, iteratively generating an assessment of the entire set of plans and schedule across the continuum. Combining data and information between SD and DES models and techniques assures relevance to the stakeholder needs and effective problem scoping and scaling that can also evolve with dynamic architecture and policy requirements. An example case study shows the combined use of the two models and how they are used to evaluate a typical scenario where increased demand is placed on the training continuum. The multi-level approach provides a high level indication of training requirements to the model of the new training school, where the detailed model indicates the resources required to achieve those particular student levels.

Goal: this study aimed to use the experimental simulation method to obtain the optimum results by using the simulation technique of the models, which are modeled mathematically but not solved.
 Design/Methodology/Approach: a case study was conducted to apply the discrete-event simulation (DES) approach based on the design of experiment (DOE) technique to derive strategies and analyze situations in an emergency department (ED).
 Results: by integrating the DOE approach with the DES technique for the ED, the average waiting time for a patient in a small ED was reduced from 40.09 minutes to 9.83 minutes (75.48%), which is a significant result. Increasing the capacity of the resources without increasing the number of daily resources for the ED resulted in an increase from 133 to 147 (10.53%) in the number of patients treated. Additionally, the optimum number of healthcare resources to employ in EDs was calculated.
 Limitations: there is no shift system for healthcare resources in the DES model. Since the ED was considered in this study, the same number of resources was run during the working hours of the DES model.
 Practical implications: for researchers and healthcare professionals, the developed method is used to obtain results that cannot be obtained through the mathematical modeling of healthcare problems. The results of the changes in health resources on the outputs are obtained by this method as the shortest time frame and lowest cost.
 Originality/Value: there is almost no detailed study of DES with the DOE technique. Providing high reliability has resulted in obtaining optimum values through the integration of the DOE approach with the DES technique in the healthcare area. It is very difficult to get accurate results anywhere where the human factor is present.

International audience

An integrated approach of discrete event simulation and a non-radial super efficiency data envelopment analysis for performance evaluation of an emergency department

Movement of entities in discrete event simulation typically requires predefined paths with decision points that dictate entity movement. Human-like travel is difficult to model correctly with these constraints because that is not how people move and large individual differences exist in capabilities and strategies. Agent based modeling is considered a better way to simulate the real-time interaction of people with their environment. In this paper we propose to integrate agent based modeling with discrete event simulation to simulate the movement of people in a discrete event system. An agent based module was constructed within the AutoMod simulation package, and a test case was modeled in which people (agents) at a theme-park interact with objects and people in their environment to get directions and then walk or take a tram to their final destination. We explain the details of model implementation and describe the verification and initial validation of the model.